Regional Studies, Regional Science ISSN: (Print) 2168-1376 (Online) Journal homepage: https://www.tandfonline.com/loi/rsrs20 Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation Kristinn Hermannsson, Rosario Scandurra & Marcello Graziano To cite this article: Kristinn Hermannsson, Rosario Scandurra & Marcello Graziano (2019) Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation, Regional Studies, Regional Science, 6:1, 539-556, DOI: 10.1080/21681376.2019.1680313 To link to this article: https://doi.org/10.1080/21681376.2019.1680313 © 2019 The Author(s). Published by Informa View supplementary material UK Limited, trading as Taylor & Francis Group Published online: 25 Oct 2019. Submit your article to this journal Article views: 547 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rsrs20 REGIONAL STUDIES, REGIONAL SCIENCE 2019, VOL. 6, NO. 1, 539–556 https://doi.org/10.1080/21681376.2019.1680313 Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation a b Kristinn Hermannsson , Rosario Scandurra and Marcello Grazianoc ABSTRACT The economic impact of tertiary education is important for regional development, and whilst participation rates have increased, it is unclear whether this has benefited regions equally. The paper analyses a panel of European regions to determine how the geography of tertiary education has evolved between 2002 and 2012. The results show a mixed picture. Overall, the system is characterized by path dependency, with the past being the best predictor of the future. There are some signs that the most lagging regions in 2002 are catching up, with some benefiting from recently opened institutions. Meanwhile, the very top-performing regions are breaking away from the rest, showing above-average growth, especially in the case capital regions. This work contributes to the ongoing research on the role of higher education in fostering regional economic development, and the emerging inequalities across European regions. ARTICLE HISTORY Received 6 December 2018; Accepted 13 September 2019 KEYWORDS tertiary education; higher education; universities; spatial inequality; economic impact; path dependency JEL CLASSIFICATIONS I20; I23; R12; R58; I28 INTRODUCTION This paper examines the regional distribution of tertiary education (TE) in Europe with the aim of identifying whether recent growth of the sector has served to promote equitable geographical distribution or if it has entrenched existing centres of excellence. As we review in the next section, a large evidence base documents the diverse channels through which the activities of tertiary edu- cation1 institutions (TEIs) benefit their host regional economies. As a result, governments have sometimes taken explicit steps to decentralize the provision of TE in order to achieve regional policy aims (e.g., Andersson, Quigley, & Wilhelmson, 2004, for the case of Sweden). However, there are ongoing concerns about regional inequality in TE activity, which has crystalized in debates about the primacy of equity or excellence in European science funding (see The Guild CONTACT a Robert Owen Centre, School of Education, University of Glasgow, Glasgow, UK. (Corresponding author) rosario.scandurra@uab.cat b Department of Sociology, Universitat Autònoma de Barcelona, Barcelona, Spain. c Department of Geography & Environmental Studies, Central Michigan University, Mount Pleasant, MI, USA. Supplemental data for this article can be accessed at https://doi.org/10.1080/21681376.2019.1680313 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 540 Kristinn Hermannsson et al. of European Research-Intensive Universities, 2018, for an overview). The recent rise in political extremism (Rodríguez-Pose, 2018) lends these debates urgency, as although in a global context the European Union (EU) is a club of advanced economies, stark cross-country (e.g., Bartkowska & Riedl, 2012; Prutvot, Estermann, & Kupriyanova, 2017) and intra-country (e.g., Gardiner, Martin, Sunley, & Tyler, 2013) differences persist (OECD, 2017). Long-term trends in participation rates follow an upward tendency (Jöns & Hoyler, 2013; Lee & Lee, 2016) and the recent history of the EU is no exception (e.g., Brooks, 2018). Over the decade from 2002 to 2012, the TE sector in the average European region expanded by about 30%. Academically, this is an interesting case to examine as it is not clear a priori how the geography of TE should evolve. In terms of its input structure education is an extreme case, relying almost solely on value added (labour, capital), in contrast to manufacturing sectors, which are dependent on intermediate inputs and therefore often cluster geographically around particular supply-chains or natural resources. In terms of final demand, TE relies heavily on pub- lic sector funding (although by no means entirely; Hermannsson, Lisenkova, McGregor, & Swales, 2014), and therefore should be sensitive to the priorities of public policy. Following this logic, TE is prima face an ideal economic development tool, offering the possibility of directing activities with significant local economic impacts to lagging regions. Conversely, it is likely to favour the same location amenities as other knowledge-intensive sectors (Saxenian, 1996; Viladecans-Marsal & Arauzo-Carod, 2012; Wenting, Atzema, & Frenken, 2011), and there is some evidence to suggest students prefer to study in economically buoyant regions (Dotti, Fratesi, Lenzi, & Percoco, 2013). These characteristics of TE could make it similar to other knowledge industries, where local ‘buzz’ and branding reinforce existing paths of agglom- eration (Bathelt, Malmberg, & Maskell, 2004). To address these issues, we construct a macro-panel of European NUTS-2 (and NUTS-1) regions between 2002 and 2012. We examine regional TE enrolment ratios, accounting for both local and incoming students, combined with a range of indicators on local economic and social context. Overall, The results reveal a pattern of path dependency, with the strongest predic- tor of success being past success. However, this is not a uniform picture. There are signs of catch up as lagging regions exhibit above-average growth and regions with high regional tertiary enrolment ratios (RTERs) are showing signs of saturation with no or negative growth on average. Conver- sely, there are also signs of divergence, where the regions with the highest enrolment ratios, that is, those that are already successful at attracting mobile students exhibit accelerating growth. There- fore, we conclude that whilst low-level regions are catching up and high-level regions are mostly stagnant, the very top regions appear to be breaking away from the rest. These trends parallel those of income dynamics globally (Alvaredo, Chancel, Piketty, Saez, & Zucman, 2018; Milanovic, 2013), and are consistent with the recent literature focussing on overall economic performance of EU regions (e.g., Iammaringo, Rodriguez-Pose, & Storper, 2018), where a few highly devel- oped and innovative regions have been identified as outperforming most other regions. The paper is structured as follows. The next section provides a brief summary of the wide- ranging literatures on the local economic impact of TE. The construction of the data set and choice of analytical methods are outlined in the third section. The fourth section describes the levels and changes in regional tertiary enrolment across 2002 and 2012. The fifth section analyses the strength of inter-temporal persistence in tertiary enrolment and explores what other factors play a role in driving change. Brief conclusions are provided in the sixth section. LITERATURE REVIEW: REGIONAL ECONOMIC DEVELOPMENT AND TERTIARY EDUCATION There is a large and diverse literature on the regional economic impact of TE. For an overview of the academic literature,2 see Florax (1992), Drucker and Goldstein (2007), Goldstein (2009), REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 541 Hermannsson (2016) and Harrison and Turok (2017). This focuses mainly on higher education, but to a lesser extent on further education (e.g., Hermannsson, Lisenkova, Lecca, McGregor, & Swales, 2017). We group studies of these phenomena into three broad categories: those focusing on impact on the demand-side of the economy, the supply-side and on economic geography. A long tradition in regional economics examines the expenditure impacts of TEIs and their students. From this perspective, TEIs contribute to the local economy by employing staff and purchasing goods and services for their operations (Hermannsson, Lisenkova, McGregor, & Swales, 2013, 2014). Similarly, students are treated as a source of exogenous consumption expenditures in the local economy, somewhat like tourists (Florax, 1992; Hermannsson, McGregor, & Swales, 2018; Love & McNicoll, 1988; Steinacker, 2005). The methodologies used to quantify these impacts in academic research are well established and draw on the principles of national accounting. Furthermore, by their nature, expenditure impacts are closely tied to the location of the TEI, that is, their benefits tend to concentrate within the region of the TEI. TEIs exert a range of supply-side impacts, enhancing the productive capacity of the local economy. An obvious channel is the human capital of graduates as gauged by higher wages (Blundell, Deardren, & Sianesi, 2005; Bradley & Taylor, 1996; Checchi, 2006; Harmon & Walker, 2003; Hermannsson, Lisenkova, Lecca, Swales, & McGregor, 2014; Psacharopoulos & Patrinos, 2004). Moreover, graduates produce externalities such as on the wages of non-skilled workers (Moretti, 2004a, 2004b). Similarly, it has been argued that the presence of TEIs (Anse- lin, Varga, & Acs, 1997) and graduates (Faggian & McCann, 2008, 2009) create knowledge externalities in the local economy and boosts innovation. For an overview of this literature, see Acs (2009). Furthermore, a range of wider impacts have been identified such as non-pecuniary benefits for the graduate population itself, such as improved health, marital success, happiness and family outcomes (McMahon, 2004, 2009). This is in addition to potential socioeconomic feedback, such as on crime (Machin, Marie, & Vujić, 2011). For a discussion of the potential local economic impact of such wider benefits, see Hermannsson et al. (2017). Although there is strong evidence to support positive impacts, in principle there is more ambiguity as to the exact magnitude of these effects (McMahon, 2004, 2009). From a local perspective, an attractive feature of a successful cluster of TEIs is their apparent ability to shape economic geography by drawing into the region and retaining research and devel- opment (R&D) activities (Andersson, Gråsjö, & Karlsson, 2009; Jaffe, 1989) and highly skilled workers (Abel & Deitz, 2012; Ahlin, Andersson, & Thulin, 2018; Beeson & Montgomery, 1993; Bound, Groen, Kézdi, & Turner, 2004; Groen, 2004; Winters, 2011). Crucially, unlikely, say, human capital effects, location effects are a zero-sum game where one region’s success draws in resources from another. A lively area of research has been the mobility of students and graduates. Evidence is mixed as to what attracts students to regions and the extent to which they are subsequently retained in the region as graduates. In a study of US graduates, Groen (2004) finds a significant link between studying in a state and working in it, although the magnitude of the impact was quite modest with approximately 10 of every 100 students living in the state of study 10–15 years after gradu- ation. Bound et al. (2004) point out that graduates are quite mobile and find that at a state level in the United States there is only a modest link between production of graduates within a state and the build-up of a graduate workforce. Venhorst, Van Dijk, and Van Wissen (2011) find that in the Netherlands graduate migration is primarily dependent on the spatial distribution of suitable jobs. Based on Italian data, Dotti et al. (2013) argue that local labour markets are major drivers of TE-related migration, and that often the migration decision of both students and graduates is a joint one, with students choosing to study in cities with attractive labour markets for graduates. Conversely, the results of Faggian and McCann (2008) indicate that for the UK the two REGIONAL STUDIES, REGIONAL SCIENCE 542 Kristinn Hermannsson et al. decisions are separate with students being drawn to lower living costs in the north, whilst Greater London is the pre-eminent destination for graduates. Evidence from both the UK and the Netherlands suggests migration is selective, with graduates from more selective institutions and courses being more likely to move (Faggian & McCann, 2008, 2009; Venhorst, Van Dijk, & Van Wissen, 2010). To summarize, a successful TE sector brings immediate benefits through spending in the local economy, can make the region a more attractive location for people and businesses, and can generate long-term benefits through increase in human capital, socioeconomic feedback and spillovers. Because of the overall benefits associated with TE, it is not surprising that this sector has featured prominently in regional policy, and cases of higher education expansion and decentralization have been documented (e.g., Andersson et al., 2004, for the case of Sweden; and Goldstein & Drucker, 2006, for the United States). Similarly, many authors have studied universities as part of particular cities and argue they play a key role as urban developers and anchor institutions (Ehlenz, 2016; Goddard, Coombes, Kempton, & Vallance, 2014; Perry & Wiewel, 2005). Whilst there is rich evidence on a range of economic impact of TE, these literatures take the geography of the sector as a given and do not attempt to analyse how it has evolved and is likely to evolve, a lacuna we aim to fill. DATA The data are obtained from EUROSTAT, which collects data at the NUTS-1 and -2 levels for a range of educational, social and economic indicators. The time span ranges from 2002 to 2012. This encompasses a decade during which higher education in Europe underwent radical changes. Germany, Portugal, Slovenia and the UK do not provide data at the NUTS-2 level (our preferred areal unit of aggregation). For these countries, we use the NUTS-1 level data, which corresponds to larger territorial units (e.g., Landers for Germany), an approach previously used in other regional analyses (Copus, 2011). The main variable of interest is calculated from an indicator produced by EUROSTAT that shows the share of students in each region in relation to the local population. We refer to this as the regional tertiary enrolment ratio (RTER), which we designate for the i-th region as Ei . This ratio is made up of a numerator encompassing the number of students of enrolled in TE pro- grammes (ISCED 5–6) residing in region i (Si ). To control for scale, the regional population in region i aged 20–24 is used as a denominator: Si Ei = (1) Pi20−24 A benefit of the RTER (Ei ) is that it can be obtained for most EU member states. It is an aggregate indicator that can be thought of as a composite of three underlying factors: (1) the regional participation rate; (2) the retention of students locally; and (3) the attractiveness of the region to mobile students. In turn, these factors are affected by various contextual elements and policies, such as amenities, education funding, student grants, labour and hous- ing markets. A drawback of the data is that it does not reveal the composition of TE students in each region, whether by origin, nature of institution or programme subject. Therefore, an implicit assumption in this analysis is that student numbers can be taken as a proxy for overall activity levels in TE. This is a reasonable assumption at a large spatial scale such as NUTS-2, where each region contains a range of different types of institutions and courses and aggregating across these will reduce variation in research and staffing intensities. Further control variables used in REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 543 Table 1. Summary statistics. Variable Observations Mean SD Minimum Maximum Regional Tertiary Enrolment Ratio, 260 58.75512 26.40706 6 220.5 year 2012 Regional Tertiary Enrolment Ratio, 230 49.90648 21.31634 3.3 137.6 year 2002 Regional Tertiary Enrolment Ratio, 227 0.2980925 0.7401884 −0.4290865 8 relative change GDP, year 2002 265 9.821788 0.4725397 8.34284 11.58 Population density, year 2002 258 4.985687 1.174152 0.7884574 8.719203 Employment. 25–34, year 2002 245 4.325081 0.1193099 3.7281 4.495355 GDP, relative change 265 0.2986735 0.278558 −0.1116751 1.629921 Unemployment 20–64, relative 233 0.4101631 0.7982152 −0.654902 3.261905 change Life expectancy, relative change 251 −0.0178194 0.2202581 −1 0.0742296 Population Density, relative change 258 0.0346699 0.068857 −0.1555977 0.3181818 the data set are obtained from EUROSTAT. Summary statistics of key variables are provided in Table 1. TERTIARY ENROLMENT RATIOS ACROSS REGIONS As can be gauged from the second line of Table 1, RTERs differed widely across the 230 Euro- pean NUTS-2 regions for which data were available in 2002. For the median region, this is just under 50%, whilst for 90% of regions this is at or below 78%. The highest value is 137.6%, observed for Vienna in Austria, whilst the lowest observation was 3.7%. Figure 1 shows a visual representation of how tertiary enrolment ratios varied across regions in 2002. A decade later, in 2012, observations are available for 30 more regions. Whilst in aggregate the RTER has only grown modestly over this 10-year period, from 49.65% in 2002 to 53.65% in 2012, more dramatic changes have occurred at both tails of the distribution. Those regions at the bottom 10th percentile now have a tertiary enrolment ratio of 37.2%, up 13.75 percentage points from 23.45% in 2002. Likewise, those at the top 90th percentile now stand at 89.5%, up 10.85 percentage points from 78.65% in 2002. The biggest change though has occurred among the top- performing regions, as the top 1% of regions has jumped 45.8 percentage points to 151.4% in 2012 and the top-performing region stands at 220.5%. The geographical distribution of tertiary enrolment in 2012 is mapped in Figure 2. There are now 46 regions where Ei > 75%, many of which are in Eastern and Southern Europe. Notably, the concentration of TE students in the UK has shrunk for most regions, and no region now registers Ei > 75%. This is likely due to tighter visa regulations, which severely restricted oppor- tunities for overseas students enrolling onto short duration courses outwith the formal higher and further education sectors.3 From Figure 2, we can observe the ‘pull’ effect of capital regions in this period. This is particularly evidence for Eastern Europe, Mediterranean and Benelux countries, where capital regions grow in excess of national averages. The scale of change across NUTS-2 regions is further highlighted in Figure 3. With a few exceptions,4 most notably the UK, most regions exhibit growing tertiary enrolment ratios. A few regions even register growth in excess of 100%. For example, this is the case of Drenthe, Fle- voland and Val D’Aosta where the tertiary enrolment was more than five times higher than a dec- ade before. It is interesting to note here that these three regions host relatively new TEIs, either REGIONAL STUDIES, REGIONAL SCIENCE 544 Kristinn Hermannsson et al. Figure 1. Regional tertiary enrolment ratio (RTER) (Ei) by region in 2002. Figure 2. Regional tertiary enrolment ratio (RTER) (Ei) by region in 2012. REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 545 Figure 3. Percentage change in enrolment ratio (Ei) by region between 2002 and 2012. created through merging and expanding previously existing institutions (in Flevoland in 1986 and in Drenthe in 2008), or through the establishment of brand new universities (e.g., in Val d’Aosta in 2000). The data presented in Figures 1–3 are available in the online appendix. Table 2 provides summary statistics for levels and growth for each country, inversely ranked by growth rate. Average enrolment has decreased for three countries: the UK, Sweden and Lat- via, whilst the remaining 25 countries register growth mostly below 1%. The biggest change in the mean is observed for Greece (EL), which grew by nearly 48 percentage points.5 Six countries, Austria (AT), Czech Republic (CZ), Greece (HE), the Netherlands (NE), Slovakia (SK), and Spain (ES) registered mean growth of more than 20 percentage points. In many cases, the national mean masks large intra-country heterogeneity. For instance, in Austria, the coefficient of variation shows 1 SD (standard deviation) equals 91% of the mean. The RTER is strongly correlated with the level of economic development. Table 3 shows the RTER at the start of the period by gross domestic product (GDP) quartiles. The regions with the lowest GDP (quartile 1) also exhibit the lowest TER at 29.47 on average. However, it should be noted that within these quartiles there is substantial variation, so the average picture clearly masks much underlying heterogeneity. WHAT DRIVES THE CHANGE? From the point of view of regional policy, it is vital to understand the extent to which the changes in the concentration of TE activities are driven by low RTER regions catching up, or whether they are reinforcing existing agglomerations. To test for this, we estimate an autoregressive model in logs, following an approach similar to that applied in the intergenerational income mobility literature (e.g., Blanden, Gregg, & Macmillan, 2007), which we interpret as a regional intertemporal persistence model: ln(y1 ) = an + bln(y0 ) + gln(Ck ) + m (2) REGIONAL STUDIES, REGIONAL SCIENCE REGIONAL STUDIES, REGIONAL SCIENCE 546 Table 2. Regional tertiary enrolment ratios (RTERs) by country in 2002 and 2012, ranked by difference. Kristinn Hermannsson et al. 2002 2012 Country Country code Mean SD Frequency CV Mean SD Frequency CV Difference, 2002–12 UK UK 62 11.72 40 0.19 57.1 5.17 40 0.09 −4.9 Sweden SE 74.2 13.00 8 0.18 69.7 12.42 8 0.18 −4.5 Latvia LV 68.3 – 1 – 65.8 – 1 – −2.5 Estonia EE 62.7 – 1 – 66.2 – 1 – 3.5 Finland FI 87.2 – 92.8 24.57 5 0.26 5.6 France FR 50.8 11.82 25 0.23 57.4 9.82 22 0.17 6.6 Italy IT 54.6 22.45 19 0.41 61.2 22.78 21 0.37 6.6 Luxembourg LU 11.4 – 1 – 18.9 – 1 – 7.5 Germany DE 45.4 12.40 38 0.27 55.1 9.21 38 0.17 9.7 Belgium BE 57.1 32.03 11 0.56 68.5 34.08 11 0.50 11.4 Poland PL 60.6 15.09 16 0.25 72.3 20.19 16 0.28 11.7 Romania RO 32.8 23.71 8 0.72 44.5 40.47 8 0.91 11.7 Ireland IE 54.1 11.95 2 0.22 68.1 20.22 2 0.30 14 Hungary HU 45.3 19.71 7 0.44 59.6 22.98 7 0.39 14.3 Portugal PT 51.6 – 67.4 27.77 7 0.41 15.8 Malta MT 24.4 – 1 – 41.4 – 1 – 17 Denmark DK 61.8 – 79.1 16.67 5 0.21 17.3 Cyprus CY 26 – 1 – 44.2 – 1 – 18.2 Lithuania LT 63 – 1 – 81.5 – 1 – 18.5 Slovenia SI 65.7 – 84.4 – 18.7 Bulgaria BG 40.2 – 59.9 29.01 6 0.48 19.7 Spain ES 58.1 12.14 17 0.21 79.7 21.66 19 0.27 21.6 Netherlands NL 53.3 24.71 12 0.46 75.6 13.53 12 0.18 22.3 Slovak Republic SK 32.5 37.60 4 1.16 55.8 90.83 4 1.63 23.3 Austria AT 46.4 41.99 9 0.91 70.8 44.23 9 0.62 24.4 Czech Republic CZ 35.1 31.90 8 0.91 65.6 64.30 8 0.98 30.5 Greece EL 64.3 – 112 46.78 4 0.42 47.7 Croatia HR 56.5 4.10 2 0.07 56.5 Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 547 Table 3. Regional tertiary enrolment ratio (RTER) 2002 by gross domestic product (GDP) quartile. Mean SD Frequency GDP quartile 1 29.47 25.32 68 GDP quartile 2 35.67 21.97 66 GDP quartile 3 48.02 21.78 65 GDP quartile 4 51.17 29.99 66 Total 40.97 26.47 265 where ln(y1 ) is the natural logarithm of the dependent variable, that is, the tertiary enrolment ratio in 2012; an is a group effect according to the country each region belongs to; bln(y0 ) is the persistence term showing the influence of the level of tertiary enrolment in 2002; and ln(Ck ) is a vector of control variables specified as log of levels in the base period, that is, 2002. Table 4 presents estimates of a simple version of this model, omitting country dummies. In our baseline specification we only include the persistence term and no controls. This shows a strong persistence effect, with approximately 55% of the level in 2012 being explained by the level of tertiary enrolment in 2002. In the second specification of this model, we control for the regional GDP and population density, but these are not statistically significant. In models (3) (a–d) we fit our baseline model separately, splitting it into four quartiles based on regional per capita GDP (purchasing power parity adjusted) in 2002. This partition of the main data set allows one to investigate whether the path-dependency affects all regions equally at different income levels. These results reveal some heterogeneity in the way the persistence effect plays out. The per- sistence effect is strongest for the poorest and the richest regions, while the second-highest group by GDP (3) shows the lowest level of persistence. This group is composed almost entirely of regions within North Western Europe, typically second-tier regions outside capital regions and major commercial centres. This is consistent with previous findings, such as by Groen (2004) and Venhorst et al. (2011), that the availability of stronger job markets may influence the ability of regions to retain and to attract students but that this is counteracted by house prices Table 4. Persistence model. Gross domestic product (GDP) quartile (3) Variables Baseline (1) Full (2) 1st (a) 2nd (b) 3rd (c) 4th (d) RTER, ln, t0 0.553*** 0.528*** 0.709*** 0.566*** 0.273*** 0.622*** (0.0542) (0.0587) (0.0842) (0.0707) (0.0941) (0.0946) GDP, ln, t0 0.0549 (0.0521) Density, ln, t0 0.00559 (0.0217) Youth empl., ln, t0 −0.0678 (0.139) Constant 1.877*** 1.704*** 1.336*** 1.777*** 2.941*** 1.636*** (0.209) (0.564) (0.314) (0.256) (0.369) (0.370) Observations 227 204 46 52 61 57 R2 0.565 0.562 0.698 0.579 0.297 0.623 R 2 adjusted 0.564 0.557 0.695 0.575 0.292 0.620 Notes: *p < 0.10, **p < 0.05, ***p < 0.010. Determinants of 2012 Regional Tertiary Enrolment Ratio. The dependent variable is the natural logarithm of the enrolment ratio in 2012. An observation is a NUTS-2 region in year t. Cluster-robust standard errors are shown in parentheses. REGIONAL STUDIES, REGIONAL SCIENCE 548 Kristinn Hermannsson et al. (Dotti et al., 2013). With the second fastest median GDP growth over the period, the regions in the second quartile are possibly those better positioned to free themselves from past their past path: the economic opportunities in those regions are still abundant, yet cost of living and other negativities present in the highest GDP regions are yet to unfold. These initial results suggest an inability among the poorest regions to catch up with the rest of the EU even with an average stronger GDP growth, and a generalized convergence among the wealthiest ones. The TE sector in each region is embedded within the funding and regulatory framework of each member state and therefore likely to be influenced by national-level policies. For this reason, we rerun our models accounting for national-level group effects. Table 5 shows the results for the same models, this time including country dummies to capture the influence of group-wide shocks that are specific to each national TE system, and, more broadly, to each nation’s economy. The baseline specification (model 1) is analogous to that observed previously, but when allowing for country dummies, the influence of persistence is stronger. Nationwide policy and economic shocks affect the relative success of regions within a European comparison, but once this is con- trolled for the intra-group component of the tertiary enrolment ratio is more strongly predicted by the past state. The overall variance explained by the model increases from 55.7% to 78.9%. On average, the level of TE enrolment in 2002 explains 67.1% of the enrolment in 2012. In model 2, we control for GDP and population density. These two variables enable one to control for relative urbanization and economic performance of each region. Once we include country dummies, the regional GDP is found to explain around 26.5% of the level in 2012. When we partition the sample by GDP quartiles based on the values in 2002 (models (3a– 3d)), the results qualitatively mirror our findings in the absence of countries dummies, with an increase in the coefficients. In model (3a), we see that for the poorest regions in the EU, more than the whole of the level in 2012 can be explained by the level in 2002. In other words, any change in the period is driven by national-level policy shocks, which effectively ‘locked in’ a path of lower TE growth. The second poorest regions also seem to follow a similar path, although Table 5. Persistence model. Gross domestic product (GDP) quartile (3) Variables Baseline (1) Full (2) 1st (a) 2nd (b) 3rd (c) 4th (d) RTER, ln, t0 0.671*** 0.599*** 1.008*** 0.791*** 0.327*** 0.613*** (0.0684) (0.0771) (0.0865) (0.105) (0.0939) (0.104) GDP, ln, t0 0.265** (0.124) Density, ln, t0 0.00176 (0.0158) Youth employment, ln, t0 0.0751 (0.190) Constant 1.596*** −1.196 0.724** 1.140*** 2.712*** 1.792*** (0.236) (0.801) (0.276) (0.214) (0.349) (0.438) Observations 227 204 46 52 61 57 R2 0.777 0.801 0.872 0.900 0.727 0.905 Countries dummies YES YES YES YES YES YES R 2 adjusted 0.766 0.789 0.854 0.889 0.703 0.894 Notes: *p < 0.10, **p < 0.05, ***p < 0.010. Determinants of 2012 tertiary education enrolment with country dummies. The dependent variable is the natural logarithm of the enrolment ratio in 2012. An observation is a NUTS-2 region in year t. Cluster-robust standard errors are shown in parentheses. REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation Table 6. Relative change in tertiary education ratio. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) RTER t0 −0.0610*** −0.0662*** −0.0607*** −0.0593*** −0.0607*** −0.0651*** −0.0606*** −0.0604*** −0.0605*** −0.0655*** −0.0670*** (0.0164) (0.0193) (0.0199) (0.0179) (0.0198) (0.0172) (0.0167) (0.0161) (0.0161) (0.0198) (0.0249) RTER t0, 0.000399*** 0.000446*** 0.000393*** 0.000386*** 0.000396*** 0.000422*** 0.000396*** 0.000387*** 0.000388*** 0.000426*** 0.000433** (squared) (0.000125) (0.000157) (0.000147) (0.000134) (0.000146) (0.000129) (0.000126) (0.000119) (0.000119) (0.000152) (0.000180) GDP RC −0.0737 0.663*** 1.266* (0.177) (0.175) (0.723) Unemp. 15– 0.0915* 24 RC (0.0533) Unemp. 20– 0.131** 0.147* −0.146 64 RC (0.0661) (0.0779) (0.284) Neet 18–24 0.0877 RC (0.106) Life exp. RC 0.170 −0.121 −0.143 (0.157) (0.101) (0.125) Empl. 25–34 −0.460 RC (0.396) Pop. dens. RC 1.402 1.354 3.471 (0.959) (1.430) (2.951) Pop. RC 1.425 (0.977) REGIONAL STUDIES, REGIONAL SCIENCE Constant 2.176*** 2.331*** 2.160*** 2.097*** 2.164*** 2.330*** 2.171*** 2.133*** 2.133*** 2.074*** 1.677*** (0.500) (0.594) (0.598) (0.533) (0.597) (0.528) (0.507) (0.476) (0.474) (0.564) (0.455) Observations 227 216 181 209 206 212 215 218 220 191 191 R2 0.386 0.407 0.358 0.373 0.355 0.417 0.383 0.394 0.397 0.452 0.566 Countries NO NO NO NO NO NO NO NO NO NO YES Dummies R 2 adjusted 0.383 0.403 0.353 0.369 0.351 0.413 0.379 0.390 0.392 0.443 0.536 Notes: *p < 0.10, **p < 0.05, ***p < 0.010. The dependent variable is the relative change in the enrolment ration between 2002 and 2012. An observation is a NUTS-2 region in year t. Cluster-robust standard errors are shown in parentheses. 549 550 Kristinn Hermannsson et al. Figure 4. Simulated curve of relative change (all controls as in Table 6, (11)). the persistence level is lower (79.1%). Overall, the size of the persistence effects increase for all GDP quartiles, except the richest one, which decreases marginally (−1.5%). Change The results from the previous section suggest a high level of path dependency, thus leading one to look deeper on the determinants of change in 2002–12. We explore an alternative specification, modelling the percentage change in the RTER between 2002 and 2012. This alternative specification has the advantage to directly look into the change, rather than the levels, for the same period, 2002–12. In order to analyse what drives the changes in the RTER, we specify the following model as: ∂y = an + by0 + b̂y02 + gdC + m (3) where ∂y = (y10 − y0 )/y0 is the relative change in the dependent variable from the start to the end of the period for each region r; an is a group effect according to the country each region belongs to; by0 + b̂y02 is a quadratic term to capture the influence of the initial level of the dependent variable on the rate of change; and gdC is a vector of covariates represented as relative change from the start to the end of our observations (i.e., between t0 and t10 ). We fit several variants of this model, which are presented in Table 6. Model (1), our simplest model, only includes the level of the dependent variable at the start of the period and its squared term to account for non-linearity. In subsequent specifications of the model, we add a range of covariates. Model (3) suggests unemployment has a positive impact on TE growth, in line with previous research (e.g., Dadashova, Hossler, & Shapiro, 2011; Dellas & Sakellaris, 2003) while none of the other controls has a significant effect per se. However, when we control for the rela- tive change in regional GDP, unemployment and life expectancy, we find positive and significant effect for the first two controls (10). All these results are in line with previous literature, especially in linking the goodness of regional job markets with attractiveness for students in TE (e.g., Groen, 2004). However, these covariates are weakly statistically significant and sensitive to the exact specification of the model so overall do not play a decisive role. The influence of the initial level on growth rates is robust across all specifications (models 1– 11). The quadratic term combines with the constant to form a ‘U’-shaped curve where the influ- ence of the initial starting point varies greatly across the level of the RTER. The predications of model (11) are simulated in Figure 4. We interpret this diagram as revealing a bifurcation of REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 551 results, where for the majority of regions, the rate of RTER growth declines as a higher level is reached, indicating convergence. Region’s 1 range of 0–60% is catching up, exhibiting above average, whereas the growth in regions with RTERs of 60–100% have growth rates below aver- age. However, the situation is more complex, as for those regions with RTER > 100% the growth is above average and increasing. In terms of overall numbers, these are only a few regions and could therefore be characterized as outliers, but, if taken at face value, it appears that for a small subset of regions, the usual pattern of saturation does not apply. This tendency is similar to that observed in recent findings on the dynamics of income (Alvaredo et al., 2018; Milanovic, 2013), and it further suggests that clusters of regions (sometimes termed ‘clubs’; e.g., Borsi & Metiu, 2015)6 tend to show similar behaviours based on their initial starting point and spatial aggregation, as partly found by von Lyncker and Thoennessen (2017). It is clear that past performance is the single most important variable, with our baseline model result in a R 2 of 0.38. Once we include all covariates in model 10 this goes up to 0.44. Addition- ally, when we add country-level dummies in model (11), this rises to 0.54, suggesting that the national context matters, but this further alters the standard errors of the covariates affecting their statistical significance. CONCLUSIONS We have examined changes in the concentration of TE across European regions over a decade spanning 2002–12. This period was characterized by an overall rise in enrolment into TE, par- ticularly among new member states of the EU, catching up from a low base. Moreover, this dec- ade was marked by major economic shocks, mainly through the 2008 financial crisis and subsequent years of fiscal austerity, which have reduced public expenditures on higher education, and influenced the continent’s job market (Prutvot et al., 2017). Over this period, we observe path dependency, as the strongest predictor of the concentration of tertiary students in 2012, is the level in 2002. This result is in line with previous works high- lighting patterns of unevenness and path dependency, and they further explain the uneven per- formance of institutions (Jöns & Hoyler, 2013; Paasi, 2005). When we model the rate of change in this period, we find that the economic characteristics of a region offer little explanatory power for the development of the TE sector, which are rather influenced by nation-level policies and their overall level of development. Regions near the mean of the distribution grow relatively slowly indicating saturation, but the most dramatic changes are happening at the tails of the dis- tribution. Many regions with small sectors at the beginning of the period exhibit catch up growth and a few regions with a high share of TE activity show accelerating growth. Whilst outliers, it will be interesting to follow these highly successful TE regions over coming years to see if a bifur- cated result persists, characterized by a few top performers and everyone else. Reassuringly for regional policy-makers, it is possible to observe a high concentration of TE students in diverse regional circumstances. Unsurprisingly, many of these are rich and accessible capital regions, such as Vienna in Austria or Prague in the Czech Republic. However, we also see sparsely populated regions across Europe starting from a low base, and growing dramatically over this decade. The data show that building a new successful TE cluster is possible and policy- makers have a scope for promoting a more spatially equitable distribution of TE students. This paper draws on the best data available for comprehensive analysis of TE in Europe. Unfortunately, this is not without limitations, leaving unresolved issues for future research. The EUROSTAT data are not fully comprehensive in terms of European regions as the full range of indicators used are not available for all regions. Some of the countries involved joined the EU only recently and therefore data are not available for all years. Moreover, the TE enrol- ment ratio does not distinguish between types of programmes (e.g., professional versus academic) in which students are enrolled. Similarly, no data are available for different types of TE activities, REGIONAL STUDIES, REGIONAL SCIENCE 552 Kristinn Hermannsson et al. for example, teaching and research, which are likely to impact the local economy differently. An implicit assumption in our analysis is that the number of students represents a proxy for the over- all activity of the TE sector in the region. Future research will likely have to tackle at least some of these issues, and, when focussing on a subset of countries, use scales appropriate in relation to the decision-making processes of higher education institutions. ACKNOWLEDGEMENT The authors are grateful for feedback received from the participants at the 46th annual con- ference of the British and Irish Section of the Regional Science Association International in Harrogate, UK, the 58th annual congress of the European Regional Science Association in Cork, Ireland and the 6th Master Class on EU Cohesion Policy of the European Week of Regions and Cities. NOTES 1 A note on terminology: we adopt the term ‘tertiary education’ as this is consistent with avail- able data that is comparable across the European Union. This, in turn, is derived from interna- tionally accepted standards set out in UNESCO’s ISCED (International Standard Classification of Education). Tertiary education in this sense refers to ISCED level 5 or above. This can include qualifications from universities and other higher education institutions, as well as advanced voca- tional qualifications. The exact delimitation between these institutional types varies across Euro- pean countries and even regions (contrast, for instance, England and Scotland). However, comparable data are available for tertiary education in aggregate, which in practice includes higher education and some elements of further education. 2 Siegfried, Sanderson, & McHenry (2007) discuss the vast grey literature on this topic and cri- ticize how dubious methods are used to inflate impact for advocacy purposes. 3 This was widely discussed in the press at the time (e.g. https://www.telegraph.co.uk/news/ uknews/1502381/Crackdown-fails-to-stop-language-schools-visa-racket.html). 4 Over the decade 2002–12, new countries have joined the EU, although the mobility of their nationals was not equally guaranteed by all other members. This, among other macro-policies, may have given other Western European EU members an advantage in attracting students from new members, particularly where language constraints were smaller. 5 Notionally, Croatia (HR) exhibited a 56 percentage point growth, but data are not available to reveal the baseline in 2002. The data series for this country started in 2007. 6 To avoid confusion, here clusters or clubs are meant as partitions of the data set based on quin- tiles, rather than clustering algorithms. DISCLOSURE STATEMENT No potential conflict of interest was reported by the authors. FUNDING This research was conducted as part of the Young Adul(ll)t project funded by the European Union’s Horizon 2020 Research and Innovation Programme [grant agreement number 693167]. Rosario Scandurra also acknowledges the support of the Juan de la Cierva Grants Pro- gramme (Ref. FJCI-2016- 28588). REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 553 ORCID Kristinn Hermannsson http://orcid.org/0000-0001-9957-3914 Rosario Scandurra http://orcid.org/0000-0003-1756-2694 REFERENCES Abel, J. R., & Deitz, R. (2012). Do colleges and universities increase their region’s human capital? Journal of Economic Geography, 12(3), 667–691. doi:10.1093/jeg/lbr020 Acs, Z. J. (2009). Jaffe–Feldman–Varga: The search for knowledge spillovers. In A. Varga (Ed.), Universities, knowledge transfer and regional development (pp. 37–70). Cheltenham: Edward Elgar. Ahlin, L., Andersson, M., & Thulin, P. (2018). Human capital sorting: The “when” and “who” of the sorting of educated workers to urban regions. Journal of Regional Science, 58(3), 581–610. doi:10.1111/jors.12366 Alvaredo, F., Chancel, L., Piketty, T., Saez, E., & Zucman, G. (2018). The elephant curve of global inequality and growth. AEA Papers and Proceedings, 108, 103–108. Andersson, M., Gråsjö, U., & Karlsson, C. (2009). The role of higher education and university R&D for indus- trial R&D location. In A. Varga (Ed.), Universities, knowledge transfer and regional development: Geography, entrepreneurship and policy (pp. 85–108). Cheltenham: Edward Elgar. Andersson, R., Quigley, J. M., & Wilhelmson, M. (2004). University decentralization as regional policy: The Swedish experiment. Journal of Economic Geography, 4(4), 371–388. doi:10.1093/jnlecg/lbh031 Anselin, L., Varga, A., & Acs, Z. (1997). Local geographic spillovers between university research and high tech- nology innovations. Journal of Urban Economics, 42(3), 422–448. doi:10.1006/juec.1997.2032 Bartkowska, M., & Riedl, A. (2012). Regional convergence clubs in Europe: Identification and conditioning fac- tors. Economic Modelling, 29(1), 22–31. doi:10.1016/j.econmod.2011.01.013 Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28(1), 31–56. doi:10.1191/ 0309132504ph469oa Beeson, P., & Montgomery, E. (1993). The effects of colleges and universities on local labor markets. The Review of Economics and Statistics, 75, 753–761. doi:10.2307/2110036 Blanden, J., Gregg, P., & Macmillan, L. (2007). Accounting for intergenerational income persistence: Noncognitive skills, ability and education. The Economic Journal, 117(519), C43–C60. doi:10.1111/j.1468- 0297.2007.02034.x Blundell, R., Deardren, L., & Sianesi, B. (2005). Evaluating the effect of education on earnings: Models, methods and results from the National Child Development Survey. Journal of the Royal Statistical Society: Series A, 168 (3), 473–512. doi:10.1111/j.1467-985X.2004.00360.x Borsi, M. T., & Metiu, N. (2015). The evolution of economic convergence in the European Union. Empirical Economics, 48(2), 657–681. doi:10.1007/s00181-014-0801-2 Bound, J., Groen, J., Kézdi, G., & Turner, S. (2004). Trade in university training: Cross-state variation in the production and stock of college-educated labor. Journal of Econometrics, 121, 143–173. doi:10.1016/j. jeconom.2003.10.012 Bradley, S., & Taylor, J. (1996). Human capital formation and local economic performance. Regional Studies, 30 (1), 1–14. doi:10.1080/00343409612331349438 Brooks, R. (2018). Higher education motilities: A cross-national European comparison. Geoforum; Journal of Physical, Human, and Regional Geosciences, 93, 87–96. Checchi, D. (2006). The economics of education: Human capital, family background and inequality. Cambridge: Cambridge University Press. Copus, A. (2011). Macro Scale patterns of differentiation (spatial generalisations). In A. Copus & L. Hörnström (Eds.), The new rural Europe: Towards rural cohesion policy. NORDREGIO Report 2011:1. REGIONAL STUDIES, REGIONAL SCIENCE 554 Kristinn Hermannsson et al. Dadashova, A., Hossler, D., & Shapiro, D. (2011). National postsecondary conomisc trends: Before, during, and after the great recession. Herndon, VA: National Student Clearinghouse. Dellas, H., & Sakellaris, P. (2003). On the cyclicality of schooling: Theory and evidence. Oxford Economic Papers, 55(1), 148–172. doi:10.1093/oep/55.1.148 Dotti, N. F., Fratesi, U., Lenzi, C., & Percoco, M. (2013). Local labour markets and the interregional mobility of Italian university students. Spatial Economic Analysis, 8(4), 443–468. doi:10.1080/17421772.2013.833342 Drucker, J., & Goldstein, H. (2007). Assessing the regional economic development impacts of universities: A review of current approaches. International Regional Science Review, 30(1), 20–46. doi:10.1177/ 0160017606296731 Ehlenz, M. M. (2016). Neighborhood revitalization and the anchor institution: Assessing the impact of the University of Pennsylvania’s West Philadelphia initiatives on University City. Urban Affairs Review, 52(5), 714–750. doi:10.1177/1078087415601220 Faggian, A., & McCann, P. (2008). Human capital, graduate migration and innovation in British regions. Cambridge Journal of Economics, 33(2), 317–333. doi:10.1093/cje/ben042 Faggian, A., & McCann, P. (2009). Universities, agglomerations and graduate human capital mobility. Tijdschrift Voor Economische en Sociale Geografie, 100(2), 210–223. doi:10.1111/j.1467-9663.2009.00530.x Florax, R. J. G. M. (1992). The university: A regional booster? Aldershot: Avebury. Gardiner, B., Martin, R., Sunley, P., & Tyler, P. (2013). Spatially unbalanced growth in the British economy. Journal of Economic Geography, 13(6), 889–928. doi:10.1093/jeg/lbt003 Goddard, J., Coombes, M., Kempton, L., & Vallance, P. (2014). Universities as anchor institutions in cities in a turbulent funding environment: Vulnerable institutions and vulnerable places in England. Cambridge Journal of Regions, Economy and Society, 7(2), 307–325. doi:10.1093/cjres/rsu004 Goldstein, H. A. (2009). What we know and what we don’t know about the regional economic impacts of uni- versities. In A. Varga (Ed.), Universities, knowledge transfer and regional development (pp. 11–35). Cheltenham: Edward Elgar. Goldstein, H., & Drucker, J. (2006). The economic development impacts of universities on regions: Do size and distance matter? Economic Development Quarterly, 20(1), 22–43. doi:10.1177/0891242405283387 Groen, J. A. (2004). The effect of college location on migration of college-educated labor. Journal of Econometrics, 121, 125–142. doi:10.1016/j.jeconom.2003.10.002 Harmon, C., & Walker, I. (2003). The returns to education: Microeconomics. Journal of Economic Surveys, 17(2), 115–156. doi:10.1111/1467-6419.00191 Harrison, J., & Turok, I. (2017). Universities, knowledge and regional development. Regional Studies, 51(7), 977– 981. doi:10.1080/00343404.2017.1328189 Hermannsson, K. (2016). Economic impact of education: Evidence and relevance. In D. Wyse, E. Hayward, & J. Pandya (Eds.), The SAGE handbook of curriculum, pedagogy and assessment (Vol. 2, pp. 873–893). London: Sage. Hermannsson, K., Lisenkova, K., Lecca, P., McGregor, P. G., & Swales, J. K. (2017). The external benefits of higher education. Regional Studies, 51(7), 1077–1088. doi:10.1080/00343404.2016.1172062 Hermannsson, K., Lisenkova, K., Lecca, P., Swales, J. K., & McGregor, P. G. (2014). The regional economic impact of more graduates in the labour market: A ‘micro-to-macro’ analysis for Scotland. Environment and Planning A: Economy and Space, 46(2), 471–487. doi:10.1068/a45326 Hermannsson, K., Lisenkova, K., McGregor, P. G., & Swales, J. K. (2013). The expenditure impacts of individual higher education institutions and their students on the Scottish economy under a regional government budget constraint: Homogeneity or heterogeneity? Environment and Planning A: Economy and Space, 45(3), 710–727. doi:10.1068/a45233 Hermannsson, K., Lisenkova, K., McGregor, P. G., & Swales, J. K. (2014). ‘Policy scepticism’ and the impact of Scottish higher education institutions (HEIs) on their host region: Accounting for regional budget constraints under devolution. Regional Studies, 48(2), 400–417. doi:10.1080/00343404.2012.697139 Hermannsson, K., McGregor, P. G., & Swales, J. K. (2018). Students’ consumption expenditures in economic impact studies: Assumptions revisited in an input–output approach for Scotland. Regional Studies, Regional Science, 5(1), 57–77. doi:10.1080/21681376.2018.1428499 REGIONAL STUDIES, REGIONAL SCIENCE Will the regional concentration of tertiary education persist? The case of Europe in a period of rising participation 555 Iammaringo, S., Rodriguez-Pose, A., & Storper, M. (2018). Regional inequality in Europe: Evidence, theory and policy implications. Journal of Economic Geography, 19, 273–298. doi:10.1093/jeg/lby021 Jaffe, A. B. (1989). Real effects of academic research. The American Economic Review, 79, 957–970. Jöns, H., & Hoyler, M. (2013). Global geographies of higher education: The perspective of world university rank- ings. Geoforum; Journal of Physical, Human, and Regional Geosciences, 46, 45–59. Lee, J. W., & Lee, H. (2016). Human capital in the long run. Journal of Development Economics, 122, 147–169. doi:10.1016/j.jdeveco.2016.05.006 Love, J., & McNicoll, I. (1988). The regional economic impact of overseas students in the UK: A case study of three Scottish universities. Regional Studies, 22(1), 11–18. doi:10.1080/00343408812331344730 Machin, S., Marie, O., & Vujić, S. (2011). The crime reducing effect of education. The Economic Journal, 121 (552), 463–484. doi:10.1111/j.1468-0297.2011.02430.x McMahon, W. W. (2004). The social and external benefits of education. In J. Geraint & J. Johnes (Eds.), International handbook on the economics of education (pp. 211–259). Cheltenham: Edward Elgar. McMahon, W. W. (2009). Higher learning, greater good: The private and social benefits of higher education. Baltimore, MD: JHU Press. Milanovic, B. (2013). Global income inequality in numbers: In history and now. Global Policy, 4(2), 198–208. doi:10.1111/1758-5899.12032 Moretti, E. (2004a). Human capital externalities in cities. In V. Henderson & J. F. Thisse (Eds.), Handbook of urban and regional economics (pp. 2243–2291). Amsterdam: North Holland-Elsevier. Moretti, E. (2004b). Workers’ education, spillovers and productivity: Evidence from plant-level production func- tions. American Economic Review, 94(3), 656–690. doi:10.1257/0002828041464623 OECD. (2017). Understanding the socioeconomic divide in Europe: A background report. Paris: OECD. Retrieved from the World Wide Web: http://oe.cd/cope-divide-europe-2017 Paasi, A. (2005). Globalisation, academic capitalism, and the uneven geographies of international journal publish- ing spaces. Environment and Planning A: Economy and Space, 37(5), 769–789. doi:10.1068/a3769 Perry, D. C., & Wiewel, W. (2005). The University as urban developer: Case studies and analysis. London: Routledge. Prutvot, E. B., Estermann, T., & Kupriyanova, V. (2017). Public funding observatory report. Brussels: European University Association. Psacharopoulos, G., & Patrinos, H. A. (2004). Human capital and rates of return. In G. Johnes & J. Johns (Eds.), International handbook on the economics of education (pp. 1–57). Cheltenham: Edward Elgar. Rodríguez-Pose, A. (2018). The revenge of the places that don’t matter (and what to do about it). Cambridge Journal of Regions, Economy and Society, 11(1), 189–209. doi:10.1093/cjres/rsx024 Saxenian, A. (1996). Regional advantage: Culture and competition in Silicon Valley and route 128. Cambridge, MA: Harvard University Press. Siegfried, J. J., Sanderson, A. R., & McHenry, P. (2007). The economic impact of colleges and universities. Economics of Education Review, 26(5), 546–558. doi:10.1016/j.econedurev.2006.07.010 Steinacker, A. (2005). The economic effect of urban colleges on their surrounding communities. Urban Studies, 42 (7), 1161–1175. doi:10.1080/00420980500121335 The Guild of European Research-Intensive Universities. (2018). Mind the gap: Supporting excellence across Europe in the 9th framework programme. Retrieved from the World Wide Web: https://www.the-guild. eu/publications/guild-position-paper-widpart.pdf Venhorst, V., Van Dijk, J., & Van Wissen, L. (2011). An analysis of trends in spatial mobility of Dutch graduates. Spatial Economic Analysis, 6(1), 57–82. doi:10.1080/17421772.2010.540033 Venhorst, V., Van Dijk, J., & Van Wissen, L. E. O. (2010). Do the best graduates leave the peripheral areas of the Netherlands? Tijdschrift voor conomische en sociale geografie, 101(5), 521–537. doi:10.1111/j.1467-9663.2010. 00629.x Viladecans-Marsal, E., & Arauzo-Carod, J. M. (2012). Can a knowledge-based cluster be created? The case of the Barcelona 22@ district. Papers in Regional Science, 91(2), 377–400. REGIONAL STUDIES, REGIONAL SCIENCE 556 Kristinn Hermannsson et al. von Lyncker, K., & Thoennessen, R. (2017). Regional club convergence in the EU: Evidence from a panel data analysis. Empirical Economics, 52(2), 525–553. doi:10.1007/s00181-016-1096-2 Wenting, R., Atzema, O., & Frenken, K. (2011). Urban amenities and agglomeration economies? The locational behaviour and economic success of Dutch fashion design entrepreneurs. Urban Studies, 48(7), 1333–1352. doi:10.1177/0042098010375992 Winters, J. V. (2011). Why are smart cities growing? Who moves and who stays. Journal of Regional Science, 51(2), 253–270. doi:10.1111/j.1467-9787.2010.00693.x REGIONAL STUDIES, REGIONAL SCIENCE